MoCA-Agent: 一种用于金融和数值推理的声明市场代码智能体
MoCA-Agent: A Market-of-Claims Code Agent for Financial and Numerical Reasoning
AI总结：
提出MoCA-Agent，通过声明级验证和代码生成解决金融表格问答中的数值推理错误，在十个基准上取得强性能。
摘要：
金融和表格问答不仅需要流畅的推理：答案必须基于支持它们的确切事实、公式、单位、符号和尺度。单个误读的单元格或错误操作可能会悄无声息地产生看似合理但错误的结果。我们引入了 \textsc{MOCA-Agent}，一种声明市场代码智能体，它用声明级验证取代了自由形式的多智能体辩论。该系统将每个问题分解为类型化的原子声明，要求专业交易智能体买入或卖出这些声明，将其订单清算为置信度加权的接受/拒绝决策，并从市场支持的证据中合成可执行的Python程序。然后，一个代码感知验证器检查程序的执行、结构一致性和常见的金融推理错误，最多进行一次市场感知修复轮次。在涵盖金融数值推理、通用表格推理、ESG问答和多模态图表推理的十个公开基准上，\textsc{MOCA-Agent} 使用固定的 Qwen3.6-27B 骨干网络实现了强劲性能，包括在 FinQA 上达到 78.3%，在 FinanceMath 上达到 76.0%，在 MultiHiertt 上达到 71.2%，在 ESGenius 上达到 86.9%，以及在 FinChart-Bench 上平均达到 85.6%。这些结果表明，在原子声明级别聚合证据，而不是整个答案，提高了高风险数值推理的鲁棒性。\footnote{代码和数据可在以下网址获取：this https URL。}
英文摘要：
Financial and tabular question answering requires more than fluent reasoning: answers must be grounded in the exact facts, formulas, units, signs, and scales that support them. A single misread cell or incorrect operation can silently produce a plausible but wrong result. We introduce \textsc{MOCA-Agent}, a market-of-claims code agent that replaces free-form multi-agent debate with claim-level verification. The system decomposes each question into typed atomic claims, asks specialist trader agents to buy or sell those claims, clears their orders into confidence-weighted accept/reject decisions, and synthesizes an executable Python program from market-supported evidence. A code-aware verifier then checks the program for execution, structural consistency, and common financial reasoning errors, with at most one market-aware repair round. Across ten public benchmarks spanning financial numerical reasoning, general tabular reasoning, ESG question answering, and multimodal chart reasoning, \textsc{MOCA-Agent} achieves strong performance using a fixed Qwen3.6-27B backbone, including $78.3\%$ on FinQA, $76.0\%$ on FinanceMath, $71.2\%$ on MultiHiertt, $86.9\%$ on ESGenius, and $85.6\%$ average on FinChart-Bench. These results show that aggregating evidence at the level of atomic claims, rather than whole answers, improves robustness in high-stakes numerical reasoning.\footnote{The code and data are available: https://github.com/UBC-NLP/MoCA-Agent.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
